It is the nature of complex systems, composed of many interacting elements, that unanticipated phenomena develop. Computer simulation, in which the elements of a complex system are implemented as interacting software objects (actors), is an effective tool to study collective and emergent phenomena in complex systems. A new cognitive architecture is described for constructing simulation actors that can, like the intelligent elements they represent, adapt to unanticipated conditions. This cognitive architecture generates trial behaviors, estimates their fitness using an integral representation of the system, and has an internal apparatus for evolving a population of trial behaviors to changing environmental conditions. A specific simulation actor is developed to evaluate surveillance radar images of moving vehicles on battlefields. The vehicle cluster location, characterization and discrimination processes currently performed by intelligent human operators were implemented into a parameterized formation recognition process by using a newly developed family of 2D cluster filters. The mechanics of these cluster filters are described. Preliminary results are presented in which this GSM actor demonstrates the ability not only to recognize military formations under prescribed conditions, but to adapt its behavior to unanticipated conditions that develop in the complex simulated battlefield system.